Add a more detailed version of the inference

Add a small code snippet and more information about what is needed

README.md

@@ -10,6 +10,30 @@ datasets:
 ---
 
 # Model Card for CodeCSE
-A simple pre-trained model for code and comment sentence embeddings using contrastive learning.
+A simple pre-trained model for code and comment sentence embeddings using contrastive learning. This model was pretrained using [CodeSearchNet](https://huggingface.co/datasets/code_search_net).
 
-Instructions for inference can be found at: https://github.com/emu-se/CodeCSE
+Please [**clone the CodeCSE repository**](https://github.com/emu-se/CodeCSE) to get `GraphCodeBERTForCL` and other dependencies to use this pretrained model. https://github.com/emu-se/CodeCSE
+
+Detailed instructions are listed in the repository's README.md. Overall, you will need:
+
+1. GraphCodeBERT (CodeCSE uses GraphCodeBERT's input format for code)
+2. GraphCodeBERTForCL defined in [codecse/codecse](https://github.com/emu-se/CodeCSE/tree/main/codecse/codecse)
+
+## Inference example
+NL input example: example_nl.json
+```json
+{
+    "original_string": "", 
+    "docstring_tokens": ["Save", "model", "to", "a", "pickle", "located", "at", "path"], 
+    "url": "https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/deepq/deepq.py#L55-L72"
+}
+```
+
+Code snippet to get the embedding of an NL document ([link to complete code](https://github.com/emu-se/CodeCSE/blob/a04a025c7048204bdfd908fe259fafc55e2df169/inference.py#L105)):
+```
+nl_json = load_example("example_nl.json")
+batch = prepare_inputs(nl_json, tokenizer, args)
+nl_inputs = batch[3]
+with torch.no_grad():        
+    nl_vec = model(input_ids=nl_inputs, sent_emb="nl")[1] 
+```